Weighted Averaging for Passive Wireless SAW Sensor Interrogation
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Solution Overview
Problem
Existing wireless sensor systems for measuring tire pressure and temperature face challenges with noise and interference, leading to inaccurate frequency measurements and prolonged data update periods due to varying signal amplitudes and interference from other systems.
Innovation Solution
A method using weighted averaging of sensor responses based on power spectral density to reduce measurement errors and improve interference detection by adjusting the number of samples and using genetic optimization for optimal weighting, along with modified interference detection windows to enhance robustness and reduce DSP memory requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If simple averaging of sensor responses is used, then the measurement process is simple, but random measurement errors are large when signal amplitude is small
Solution Approach 1:
The patent changes the parameter of averaging by introducing weighted averaging where weights are derived from power spectral density values. Instead of simple uniform averaging, responses are weighted according to their signal quality, with higher weights given to responses with higher power spectral density. This resolves the contradiction by maintaining computational simplicity while significantly improving measurement precision for weak signals.
2Measurement precision
If more sensor responses are averaged to reduce random errors, then measurement accuracy improves, but data update period increases
Solution Approach 1:
The patent changes the parameter of averaging weight distribution based on power spectral density analysis. By using weighted averaging instead of uniform averaging, the system achieves better measurement accuracy with fewer samples. The weights optimize the contribution of each response based on its quality, allowing the system to reach the same accuracy level with a smaller number of averaged responses, thus reducing the data update period.
3Reliability
If traditional interference detection is used, then the system can detect interference, but DSP memory requirements are excessive
Solution Approach 1:
The patent extracts only the essential information needed for interference detection by using power spectral density values of sensor responses. Instead of storing and processing entire sensor response signals in memory, the system extracts and uses only the power spectral density metrics for weighting and interference detection. This dramatically reduces DSP memory requirements while maintaining reliable interference detection capability.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach reduces random measurement errors and shortens data update periods while maintaining accuracy, and improves the system's immunity to interference, allowing for more reliable and timely measurement of tire pressure and temperature.
Implementation Method 1
If surface acoustic wave (SAW) resonators are employed as sensing elements then their working frequency within the UHF range makes the antenna size (of around 10 cm) suitable for a wide range of practical applications. At the same time a very high Q factor of the SAW resonators around 10000 makes it possible to measure their resonant frequency wirelessly with a good accuracy.
Implementation Method 2
The basic principle behind the SAW resonant sensors is that the resonant frequency depends on the physical quantities mentioned above.
Implementation Method 3
The method most suitable for the distance of around 1-3 m has been disclosed in the GB patent 2381074 (and corresponding patent U.S. Pat. No. 7,065,459) and GB patent 2411239. The interrogation is performed in the time domain by launching an RF interrogation pulse at the interrogation frequency close to the resonant frequency of the SAW resonator that is being measured, exciting natural oscillation in the resonator, then picking up the natural oscillation after the interrogation pulse is over and analysing its spectrum.
Data Source
AI summary
A method of wirelessly interrogating a sensing device comprising a plurality of passive sensors, to determine a measurement parameter, comprises the steps of repeatedly interrogating the sensing device using a predetermined transmission signal and detecting the response; estimating the measurement parameter for each sensor by means of an analysis of the data accumulated as a result of the interrogation step, and determining the average of the parameters derived from the estimating step for each sensor, using a weighted average, in which the weightings depend on the amplitude of the sensor response. The measurement parameter may be a resonant frequency where the passive sensors are resonant devices, and the sensors may be SAW devices.